Associate Professor, Heinz College of Information Systems and Public Policy
Affiliated Faculty, Machine Learning Department
Carnegie Mellon University
Email: georgechen [at symbol] cmu.edu
I primarily work on building trustworthy machine learning models for time-to-event prediction (survival analysis) and for time series analysis. I often use nonparametric prediction models that work well under very few assumptions on the data. My main application area is in healthcare. I am supported in part by an NSF CAREER award.
Survival analysis: Much of what I work on is survival analysis. I have a new monograph (published December 2024) in Foundations and Trends in Machine Learning that aims to be a reasonably self-contained introduction to deep survival models for time-to-event prediction, targeted toward a machine learning audience; a preprint is on arXiv. Previously, I taught a survival analysis tutorial at CHIL 2020 and at SIGMETRICS 2021, and I co-organized a survival analysis symposium as part of the 2023 AAAI Fall Symposium Series.
CoolCrop: I occasionally also work on machine learning for the developing world. I co-founded and now am an advisor for CoolCrop, an AgriTech startup based in India that works on providing farmers with cold storage units (such as a refrigerator shared by a village) and market forecasts. We currently serve over 9000 farmers across 7 states in India at over 40 sites.
Pre-historic: I obtained my Ph.D. in Electrical Engineering and Computer Science at MIT. My thesis was on nonparametric machine learning methods. At MIT, I also worked on satellite image analysis to help bring electricity to rural India, and taught twice in Jerusalem for MEET, a program that brings together Israeli and Palestinian high school students to learn computer science and entrepreneurship. I completed my B.S. at UC Berkeley, majoring in Electrical Engineering and Computer Sciences, and Engineering Mathematics and Statistics.
Upcoming teaching: At CMU in spring 2025 mini 4, I will be teaching "Unstructured Data Analytics" to public policy master's students (course number 94-775) and to information systems master's students (95-865).